Materials Cloud with its five sections – LEARN, WORK, DISCOVER, EXPLORE, and ARCHIVE – aims to provide an ecosystem that supports researchers throughout the life cycle of a scientific project, and helps them make their research output FAIR and reproducible. Figure 1 illustrates how the five sections of Materials Cloud mirror the typical research cycle, from learning to simulating and, finally, to curating and publishing results, which become the starting point for new research: LEARN (described in section Education and outreach) contains educational materials and videos; WORK (section Simulation services) focuses on simulation services, turnkey solutions and data analytics tools. The three sections DISCOVER, EXPLORE, and ARCHIVE are Materials Cloud’s approach to FAIR sharing of research data (sections FAIR data and Reproducibility).

Fig. 1

Materials Cloud organises its resources in five sections, LEARN, WORK, DISCOVER, EXPLORE, and ARCHIVE, representing different stages of the research life cycle.

Specifically, the ARCHIVE is a moderated repository, where researchers can submit relevant research data from computational materials science in formats of their choice. The repository guarantees long-term storage of records and associated metadata, their findability via persistent identifiers, and their accessibility via standard protocols. While the ARCHIVE is the port of entry for all research data hosted on Materials Cloud, it can form the basis for additional, interlinked layers of accessibility, interoperability and reusability: DISCOVER allows researchers to provide visualizations that are tailored specifically to their data and act as curated points of entry for discovering the contents of the data set, similar to figures in a paper. And for research done using the AiiDA workflow manager15,16, EXPLORE provides access to the raw, automatically recorded provenance graph through an interactive browser.

In the following, we present the individual sections of Materials Cloud in detail. For a brief overview, see Table 1.

Table 1 Detailed overview of Materials Cloud sections.


The ARCHIVE and DISCOVER sections allow researchers to make their data available in a findable, accessible, interoperable, and reusable (FAIR) way6. The Materials Cloud ARCHIVE is an open-access, moderated repository for research data in computational materials science that allows researchers worldwide to upload and publish their data free of charge. In particular:

  • it provides globally unique and persistent digital object identifiers (DOIs) for every record;

  • metadata, such as title, description, keywords and license are always publicly available (Creative Commons Attribution Share-Alike 4.0 license);

  • metadata can be harvested in a number of machine-readable formats, including HTML meta tags (Dublin core), OAI-PMH (Dublin core) and JSON-LD (;

  • all data are stored at the Swiss National Supercomputing Centre (CSCS);

  • it is non-commercial and free of charge;

  • data records are guaranteed to be preserved for at least 10 years after deposition (already paid for);

  • current size limits are 5 GB for general data records and 50 GB for AiiDA databases;

  • moderators can approve larger data sets upon request (currently, 0.5 petabytes are allocated overall).

Data management plans (DMPs) that describe the handling of data both during a research project and after its completion are becoming standard components of research grant proposals. The Materials Cloud ARCHIVE is listed on the re3data17 and FAIR sharing18 repository registries, indexed by Google Dataset Search and B2FIND (, and it is a recommended repository for materials science by Nature Scientific Data. It complies with the data repository requirements of major funding agencies, and provides tailored DMP templates (

We take data stewardship seriously. In order to ensure data longevity, our storage provider CSCS is paid in advance to preserve any data set for at least 10 years after deposition, and we remain committed to continue data retention beyond this time period, if funding is available. For the unlikely scenario that a complete halt in funding for Materials Cloud prevents us from keeping the platform online, we have developed a contingency plan leveraging the long-term storage (LTS) system at CSCS. The LTS will guarantee that all registered DOIs continue to resolve to static landing pages describing the record metadata, with links to the data sets themselves.

Unlike interdisciplinary repositories for research data, such as Zenodo (, Data Dryad (, the Open Science Framework (, or figshare (, the ARCHIVE is moderated and focuses on providing added value for datasets from computational materials science. Submissions to the ARCHIVE are expected to provide data that is of value to and can be used by other researchers in the field, such as data supporting a past, present or future peer-reviewed paper. Materials Cloud moderators are subject experts, who follow a set of criteria ( to flag unsuitable or duplicate content, inappropriate form or topic, or excessive submission rates, much in the spirit of the arXiv preprint server ( While all data formats are accepted, moderators will suggest alternative formats, where applicable, that improve interoperability and reusability, in line with the 5-star deployment scheme to open web data (

Researchers can leverage the full power of the approach by adding interactive DISCOVER and EXPLORE interfaces to their datasets in order to provide further layers of accessibility, interoperability and reproducibility (see also section Reproducibility below). DISCOVER sections focus on curated data, presented in the form of tailored interactive visualisations. For example, in the DISCOVER section “2D structures and layered materials19, users can browse the curated dataset discussed in ref. 20. After selecting a material, key properties of the compound are displayed on a detail page (Fig. 2a,b), which includes interactive visualisations of quantities, such as the crystal structure, the electronic band structure, as well as phonon eigenvectors and band structures. Figure 3 shows screenshots from another DISCOVER section on “Covalent organic frameworks (COFs) for methane storage applications21, containing interactive versions of the static figures published in ref. 22. The research data underlying all DISCOVER sections on Materials Cloud is published in corresponding ARCHIVE records and citable through DOIs.

Fig. 2

DISCOVER section on “2D structures and layered materials19. The “ID card” of a material displays key computed properties, as well as interactive visualisations of the crystal structure (a), the electronic band structure (b) and more. AiiDA icons link every piece of data to its corresponding node in the provenance graph that can be browsed through the EXPLORE interface shown in Fig. 4.

Fig. 3

DISCOVER section on “Covalent organic frameworks (COFs) for methane storage applications21, presenting almost 70000 COFs assembled in silico, together with their computed properties (a) and atomic structures (b) in the form of interactive figures that mirror those published in the corresponding peer-reviewed paper.

What differentiates Materials Cloud DISCOVER sections from other approaches to presenting materials data is that each piece of data in a DISCOVER section can be linked to a node in the AiiDA provenance graph for full reproducibility, as we discuss in the following.

Reproducibility: EXPLORE

While making data FAIR simplifies and accelerates the sharing of knowledge, it is equally important to ensure that the knowledge being shared is reliable. Computational materials science involves running computer programs on digital inputs and producing digital outputs. Yet, historically, only some input and output data have been shared in the computational materials science literature, often in narrative form, making it unnecessarily difficult for peers to reproduce reported results. While storing and sharing all data may not be technically feasible or financially sensible, researchers (and reviewers) today should demand that the data provided is sufficient to reproduce the reported results in their entirety.

This simple and seemingly self-evident demand can be tedious and time-consuming to meet in practice. Researchers leave out pieces of information for a variety of reasons: data may appear trivial, irrelevant or too complex to provide in accessible form. The challenge of providing access to this data is amplified in studies that involve large numbers of materials or workflows with many different steps. The need to simplify and automate this task has been one of the drivers for the development of the AiiDA framework16,22.

AiiDA plays two main roles in the context of computational science: that of a manager of simulations, and that of a “stenographer” of events. The manager lets scientists interact seamlessly with remote high-performance computing (HPC) resources, and orchestrates computational workflows involving many steps, simulation codes, and possible paths. The stenographer records the data trail leading from the inputs to the results of a workflow, the data provenance, and stores it in databases tailored for efficient data mining of heterogeneous results. This includes information on who submitted the calculation, when the calculation was submitted, which computer and code were used, which inputs were used, which outputs were produced, as well as how these outputs are further used as inputs to the next calculation (see Fig. 4). Further details on the AiiDA provenance model can be found in the dedicated section of ref. 15.

Fig. 4

EXPLORE interface for AiiDA provenance graphs. (a) Interactive view of a calculation node (here representing a run of pw.x code from the Quantum ESPRESSO suite27), providing download links for all input and output files. The provenance browser on the right allows jumping to the visualisation of any input or output node of the calculation. (b) Interactive view of the atomic crystal structure returned by calculation (a). The provenance browser indicates that this structure was used in three subsequent calculations. See the supporting information for the complete provenance graph.

Since long-term data storage is more expensive than the short-term storage used during simulations, it is often not reasonable to preserve all output data. Which output data to store is decided by the AiiDA plug-in for the code in question – for example, in a density-functional theory calculation, total energies, electronic band structures and log files might be stored by default, while Kohn-Sham wave functions might be discarded. The overarching principle, however, is that all information needed to reproduce the outputs must be preserved, even if not all intermediate files are persisted. By combining this information pertaining to individual calculations with the logical relationships between successive calculations, AiiDA provides reproducibility of entire workflows out of the box.

Scientists who use AiiDA for their calculations can choose to upload their AiiDA databases to the ARCHIVE in order to complement their published research with a comprehensive record of their calculations. When they do so, peers can browse the AiiDA graph in the corresponding EXPLORE section, as shown in Fig. 4: nodes of the graph represent either calculations or pieces of data. Each node is linked to its parent and child nodes, which can be traversed via the provenance browser. Dedicated visualisations make the content of nodes intuitively accessible, and allow downloading individual pieces of data (such as crystal structures or input files). Subject experts, on the other hand, can install AiiDA on their computer, import the AiiDA database with a single command, and continue their own research from where the authors of the original work left off.

In this context, AiiDA plays a role similar to Git (by tracking scientific workflows) while Materials Cloud plays a role similar to GitHub (a platform to share, browse and visualise all that has been tracked by AiiDA). Rather than trying to define and enforce a global schema and ingest all submissions into one monolithic database, Materials Cloud adopts the “repository of repositories” model of GitHub et al. and provides each submission with its own space. By using the AiiDA provenance model, Materials Cloud contributors nevertheless benefit from a unified user experience for browsing and searching for data and simulations. They can rely on standardised AiiDA data types, where appropriate, while AiiDA’s flexible plug-in system supports adding new types or extending existing ones to fit the specific purpose of the research undertaken. Finally, generic application programming interfaces, such as OPTIMADE (, can operate across databases and provide features such as platform-wide crystal structure search.

Simulation services: WORK and AiiDAlab

While DISCOVER, EXPLORE, and ARCHIVE enable the dissemination of results that have already been computed, in the WORK section users can launch well-defined data generation and analytics workflows directly from the web browser, thus making these workflows accessible to a wider user base of students, experimental scientists, and computational scientists.

On the one hand, this includes stand-alone tools that run computationally inexpensive simulations and produce immediate results: ranging from tools that help plotting electronic band structures (Fig. 5a) or visualising lattice vibrations (Fig. 5b) to tools leveraging machine learning methods to predict materials properties. The underlying docker technology ( makes it possible to support a diverse set of software frameworks on the same platform, allowing for custom solutions that are adapted to the specific tool in question.

Fig. 5

Tools in the WORK section. (a) SeeK-path tool44 for finding and visualising paths in reciprocal space, here showing the Brillouin zone of InHg. (b) Interactive visualiser for lattice vibrations (adapted from, here of two-dimensional phosphorene. Shown is the phonon eigenvector (left) corresponding to the red dot in the phonon band structure (right)44.

On the other hand, the WORK section focuses on the AiiDAlab, an ecosystem for applications powered by the AiiDA workflow manager ( AiiDAlab aims to remove barriers related to the set up and installation of simulation software by providing access to applications for launching and controlling computational workflows directly from the web browser. Users log in to a private, containerised environment that provides a persistent work space for storing apps, their AiiDA database, and file repository (Fig. 6a,b). AiiDAlab apps let users connect their own computational resources anywhere in the world in order to run their workflows of interest at the scale they desire. Users can upload data to the platform either directly from their computer, or from connected open databases such as the Crystallography Open Database23 or any database implementing the OPTIMADE standard (, including AFLOW (aflow.org8), COD (, TCOD (, MPDS (mpds.io25), Materials Project (materialsproject.org9), NOMAD (nomad-coe.eu12), Open Materials Database (, OQMD (oqmd.org10), and Materials Cloud itself.

Fig. 6

AiiDAlab simulation environment. (a) Landing page with an overview of the applications installed. (b) “App store” for managing applications. (c) Application that computes the optimised crystal structure of an input material as well as its electronic band structure along standardised paths. Clicking “Edit App” switches to the source code editor (d) of the underlying Jupyter notebook.

The intuitive graphical interface makes AiiDAlab applications a great vehicle for sharing turnkey solutions with non-specialists, be it computational scientists from another discipline or experimental researchers with no programming experience.

From a technological perspective, AiiDAlab applications are Jupyter notebooks ( containing instructions for the AiiDA workflow manager, which are transformed into an interactive web application (see Fig. 6c,d). This design has two important implications for app development: First, the widespread adoption of Python and Jupyter notebooks in data science in general26 and computational materials science in particular makes most researchers in the field potential app developers. In particular, thanks to Jupyter widgets, interactive web interfaces can be written in a few lines of Python, and no longer require knowledge of JavaScript. And second, AppMode lets developers switch between the graphical app layout (Fig. 6c) and the Python development environment (Fig. 6d) at the click of a button. Apps can be edited live in the browser, and developers have the full power of the Python programming language at their fingertips.

AiiDAlab encourages the sharing of workflows and visualisations via an App store model: in a first step, developers register their application on the application registry ( Once registered, other users can then install the app via the built-in application manager (Fig. 6b) and access it from their home screen (Fig. 6a). The source codes of the AiiDAlab, AppMode, and AiiDA itself are released under the permissive MIT open-source license (see code availability statement), enabling re-deployment of the AiiDAlab platform both in academic and in corporate environments. When a local, self-contained AiiDAlab environment is desired – e.g., for educational purposes – users can download the Quantum Mobile virtual machine that runs on Linux, MacOS, and Windows (see section Education and outreach).

Education and outreach: LEARN and Quantum Mobile

The LEARN section of Materials Cloud hosts video lectures, tutorials, and seminars in computational materials science (Fig. 7a). Lectures in collaboration with CECAM ( include the “Classics on Molecular and Materials Simulations”, dedicated to record pioneering contributions in the field, and the “Mary Ann Mansigh conversations” in which outstanding representatives from computational science share their perspective on how modelling affects society. Videos are grouped by topic or event, presented together with accompanying materials, and slides where available. The Slideshot video player shows video and slides side by side, and keeps them in sync (Fig. 7b,

Fig. 7

Education and outreach. (a) MARVEL distinguished lectures available in the LEARN section. (b) Slideshot player with slide synchronisation and slide-based browsing. (c) Simulation codes provided with the Quantum Mobile virtual machine, and deployment schemes for the Desktop and Cloud Edition. (d) Screenshot of the Quantum Mobile desktop.

Besides the educational materials in the LEARN section, students can also download the Quantum Mobile virtual machine for computational materials science (Fig. 7c) from the WORK section. Quantum Mobile is based on Ubuntu Linux and comes pre-installed with a collection of open-source software packages for quantum-mechanical calculations including Quantum ESPRESSO27, Yambo28, fleur29, Siesta30, CP2K31, and Wannier9032.

Furthermore, it includes the Standard Solid State Pseudopotential Library (SSSP)33,34, various visualisation tools (jmol35, XCrySDen36, gnuplot, grace), a job scheduler (Slurm) and a build environment with C, C++ and Fortran compilers as well as scientific and MPI libraries. AiiDA and the AiiDAlab environment are pre-configured, including AiiDA plug-ins for each of the ab initio codes listed above, ready to be used out-of-the-box (Fig. 7c,d).

Quantum Mobile provides a uniform environment for quantum mechanical materials simulations and runs on most popular operating systems, including Linux, MacOS and Windows, via the VirtualBox software ( Contrary to other encapsulation strategies, such as Docker, students interact with a familiar graphical desktop, shown in Fig. 7d. Since its first release in November 2017, Quantum Mobile has been updated continuously and its Desktop and Cloud Editions have been used in lecture courses at EPFL, ETHZ, and Ghent University ( as well as in numerous tutorials on electronic structure methods, molecular simulations, and AiiDA (see, where it helps reducing the time needed for installation and configuration of software.

The modular design of Quantum Mobile takes into account that one size does not fit all: its components (simulation codes, tools, data) are encapsulated in reusable, individually tested components (see Code availability statement). Teachers can pick and choose from a growing repository of more than 30 components and build their own version of Quantum Mobile containing just the tools they need.

Source link

Leave a Reply

Your email address will not be published. Required fields are marked *